Model-Based Parameter Optimization for Ground Texture Based Localization Methods
This work addresses the challenge of optimizing parameters for robot localization methods, which is incremental as it builds on existing ground texture-based approaches.
The paper tackles the problem of efficiently parameterizing ground texture-based localization methods for robots by deriving a prediction model for localization performance that requires only a small set of sample images. The evaluation shows that the model accurately predicts the effects of changing key parameters, such as the number of extracted features, for two localization methods.
A promising approach to accurate positioning of robots is ground texture based localization. It is based on the observation that visual features of ground images enable fingerprint-like place recognition. We tackle the issue of efficient parametrization of such methods, deriving a prediction model for localization performance, which requires only a small collection of sample images of an application area. In a first step, we examine whether the model can predict the effects of changing one of the most important parameters of feature-based localization methods: the number of extracted features. We examine two localization methods, and in both cases our evaluation shows that the predictions are sufficiently accurate. Since this model can be used to find suitable values for any parameter, we then present a holistic parameter optimization framework, which finds suitable texture-specific parameter configurations, using only the model to evaluate the considered parameter configurations.